Revolutionizing heat recovery in shell-and-tube latent heat storage systems: an arc-shaped fin approach
Bibliographic record
Abstract
Strengthening the thermal response of Phase-Change Materials (PCMs) is an essential and active field of research with promising potential for advanced applications such as solar energy storage, building energy conservation, and thermal management in electronic devices.This article evaluates the efficacy of a new arc-shaped fin array in shell-and-tube heat storage systems to enhance the PCM response during the discharge mode.Different fin geometric parameters including the fin curvature angle, the fin spacing, and the nonuniform angle between fins in the top and bottom sections of the PCM domain were considered to identify the best-performing layout.The analysis shows that increasing the curvature of arc-shaped fins between 60° and 180° and increasing the fin spacing between 5 and 15 mm can significantly reduce solidifying time and improve heat recovery rates.Moreover, the arc-shaped fins are more efficient than conventional longitudinal (+-shaped) fins, which are commonly employed in thermal energy storage applications.Arc-shaped fins can also save discharge time by more than half and improve the rate of heat recovery by almost four times than that of + -shaped fins.The present findings suggest that arc-shaped fins represent a promising design for enhancing the heat-recovery aspects in PCM-based energy storage systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".